Facundo Bromberg

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We present two algorithms for learning the structure of a Markov network from data: GSMN* and GSIMN. Both algorithms use statistical independence tests to infer the structure by successively constraining the set of structures consistent with the results of these tests. Until very recently, algorithms for structure learning were based on maximum likelihood(More)
We address the problem of improving the reliability of independence-based causal discovery algorithms that results from the execution of statistical independence tests on small data sets, which typically have low reliability. We model the problem as a knowledge base containing a set of independence facts that are related through Pearl’s well-known axioms.(More)
In this paper we introduce an efficient independence-based algorithm for the induction of the Markov network structure of a domain from the outcomes of independence test conducted on data. Our algorithm utilizes a particle filter (sequential Monte Carlo) method to maintain a population of Markov network structures that represent the posterior probability(More)
In the past years, several support vector machines (SVM) novelty detection approaches have been applied on the network intrusion detection field. The main advantage of these approaches is that they can characterize normal traffic even when trained with datasets containing not only normal traffic but also a number of attacks. Unfortunately, these algorithms(More)
In this paper we introduce a novel algorithm for the induction of the Markov network structure of a domain from the outcome of conditional independence tests on data. Such algorithms work by successively restricting the set of possible structures until there is only a single structure consistent with the conditional independence tests executed. Existing(More)
Salmonella typhimurium strains, lacking both enzyme I and the phosphocarrier protein, HPr, of the phosphoenolpyruvate-sugar phosphotransferase system, cannot transport or metabolize glucose and other sugar substrates of this enzyme system. Mutants which regain the ability to specifically utilize glucose were found to constitutively synthesize a galactose(More)
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for learning the structure of the Markov network of a domain from independence tests on data. DGSIMN, like other independence-based algorithms, works by conducting a series of(More)
In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by(More)
Markov random fields provide a compact representation of joint probability distributions by representing its independence properties in an undirected graph. The well-known Hammersley-Clifford theorem uses these conditional independences to factorize a Gibbs distribution into a set of factors. However, an important issue of using a graph to represent(More)